Advanced illness patients represent only about 6.5% of the Medicare population yet they drive the majority of cost, utilization, and negative outcomes, specifically about 35% of adverse events, 50% of readmissions, and more than half of inpatient deaths.
Predictive models help to identify these specific patients early on in their hospital journey. This enables proactive clinical decision making, timely follow-up care, reduced readmissions, and higher quality care.
Video by Nassib Chamoun
Full transcript:
A common theme today that we hear from talking to a lot of hospitals and health systems is that the hospitals are overloaded at capacity. They have a major readmission problem. There’s often a mortality and adverse events problem, and their length of stay are out of control.
Now, when you look at the data, of course, it is driven by a sicker, more complicated patients, but a uniquely specific population that contributes to that are the advanced illness end of life patients. The reality is the hospital is absolutely the wrong place to manage and care for these patients. And we work with many accountable care organizations that have figured out that these patients are better supported and cared for at home.
Today, when you look at this advanced illness population, it’s about five and a half to six and a half percent, maybe 7% of the Medicare population in the community. Yet when you look at their impact, they represent over a third of admissions into hospitals, a third of adverse events, a third of the extension of length of stay days in the hospital, readmissions, 50% inpatient deaths.
They represent more than half of that it is critical that we find those patients, and we find them in real time as their condition is progressing in many of the conversations I’ve had, everybody knows these patients after the fact. They know them because they’re expensive. They know them because they’ve had multiple readmissions.
But when I ask a hospital, do you know on that day who are the patients you would treat or manage differently because they fall into this category? The answer is they don’t, and that’s the issue with a lot of the analytics today. It’s like driving on the highway looking at your rear view mirror.
So that’s where stratification and predictions of those patients in the community becomes essential, because that gives us the opportunity, not only to identify those patients, but with targeted triaging, identify the underlying conditions that challenge them on a day to day basis, and be very directed in the goals of the care we deliver to make sure that the exacerbations that land them in the hospital are managed successfully at home or in the facilities they live in, to make sure it’s a better outcome for them, better experience for them, and ultimately a better impact for the system that’s overwhelmed today by this population.
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